timesfm-forecasting
Category: data-science-and-ml · License: Apache-2.0 license · Version: 1.2 · Includes scripts
Tags: time series forecasting, foundation models, zero-shot forecasting
When to use it
Zero-shot time series forecasting with Google’s TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.Example requests
Ask your agent in plain language, for example:- “Forecast the next 30 days from this daily series, no training please.”
- “I have hourly_power.csv with a ‘load_kw’ column, about 20k hourly readings from a campus substation. I’m on a laptop with 8 GB RAM and no GPU. Can you predict the next 48 hours with an uncertainty band? I hit a ‘Model is not compiled’ RuntimeError last time I tried a pretrained forecaster.”
- “We log blood glucose from 300 patients every 5 minutes in a wide CSV, one column per patient. I don’t have time to fit a model per patient, but I need a 2-hour-ahead forecast for each of them, plus a way to flag readings that look abnormally off from what was expected.”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.
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